社会层面 · privacy论文趋势 2023–2026

AI 社会议题：privacy

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 431 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

社会层面 · privacy论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023457521
20248711136117
2025802712155
2026442902138

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

全部社会层面 · privacy论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 431 篇，第 8 / 9 页

  1. NeurIPS 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
  2. NeurIPS 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
  3. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
  4. NeurIPS 2024FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
  5. NeurIPS 2024FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
  6. NeurIPS 2024GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
  7. NeurIPS 2024Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images
  8. NeurIPS 2024GS-Hider: Hiding Messages into 3D Gaussian Splatting
  9. NeurIPS 2024HEPrune: Fast Private Training of Deep Neural Networks With Encrypted Data Pruning
  10. NeurIPS 2024Instance-Optimal Private Density Estimation in the Wasserstein Distance
  11. NeurIPS 2024Instance-Specific Asymmetric Sensitivity in Differential Privacy
  12. NeurIPS 2024Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
  13. NeurIPS 2024Large Language Model Unlearning
  14. NeurIPS 2024Measuring Dejavu Memorization Efficiently
  15. NeurIPS 2024Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
  16. NeurIPS 2024Membership Inference Attacks against Large Vision-Language Models
  17. NeurIPS 2024Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
  18. NeurIPS 2024Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
  19. NeurIPS 2024Nimbus: Secure and Efficient Two-Party Inference for Transformers
  20. NeurIPS 2024No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
  21. NeurIPS 2024Noise-Aware Differentially Private Regression via Meta-Learning
  22. NeurIPS 2024Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation
  23. NeurIPS 2024On Differentially Private Subspace Estimation in a Distribution-Free Setting
  24. NeurIPS 2024On Differentially Private U Statistics
  25. NeurIPS 2024On provable privacy vulnerabilities of graph representations
  26. NeurIPS 2024On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
  27. NeurIPS 2024On the Computational Complexity of Private High-dimensional Model Selection
  28. NeurIPS 2024Oracle-Efficient Differentially Private Learning with Public Data
  29. NeurIPS 2024OSLO: One-Shot Label-Only Membership Inference Attacks
  30. NeurIPS 2024PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
  31. NeurIPS 2024Pre-training Differentially Private Models with Limited Public Data
  32. NeurIPS 2024Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
  33. NeurIPS 2024Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
  34. NeurIPS 2024Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
  35. NeurIPS 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
  36. NeurIPS 2024Private and Personalized Frequency Estimation in a Federated Setting
  37. NeurIPS 2024Private Attribute Inference from Images with Vision-Language Models
  38. NeurIPS 2024Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
  39. NeurIPS 2024Private Geometric Median
  40. NeurIPS 2024Private Online Learning via Lazy Algorithms
  41. NeurIPS 2024Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
  42. NeurIPS 2024PrivCirNet: Efficient Private Inference via Block Circulant Transformation
  43. NeurIPS 2024Pseudo-Private Data Guided Model Inversion Attacks
  44. NeurIPS 2024Public-data Assisted Private Stochastic Optimization: Power and Limitations
  45. NeurIPS 2024RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees
  46. NeurIPS 2024Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
  47. NeurIPS 2024Revisiting Differentially Private ReLU Regression
  48. NeurIPS 2024RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation
  49. NeurIPS 2024Sample-Efficient Private Learning of Mixtures of Gaussians
  50. NeurIPS 2024Scalable DP-SGD: Shuffling vs. Poisson Subsampling

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接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查社会层面 · privacy在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "social-privacy"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

常见问题

2023–2026 年四大会议一共有多少篇社会层面 · privacy论文？
共 431 篇，按年份分别是 2023 年 21 篇、2024 年 117 篇、2025 年 155 篇、2026 年 138 篇。
NeurIPS 2026 有多少篇社会层面 · privacy论文？
NeurIPS 2026 有 44 篇社会层面 · privacy论文，上一届是 80 篇。
ICLR 2026 有多少篇社会层面 · privacy论文？
ICLR 2026 有 2 篇社会层面 · privacy论文，上一届是 2 篇。
ICML 2026 有多少篇社会层面 · privacy论文？
ICML 2026 有 90 篇社会层面 · privacy论文，上一届是 71 篇。
CVPR 2026 有多少篇社会层面 · privacy论文？
CVPR 2026 有 2 篇社会层面 · privacy论文，上一届是 2 篇。
哪个会议的社会层面 · privacy论文最多？
NeurIPS，2023–2026 年共 215 篇。

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